Part 1

  1. Read in the gapminder_clean.csv data as a tibble using read_csv.
gapminder_clean <- read_csv("gapminder_clean.csv")
datatable(gapminder_clean)
  1. Filter the data to include only rows where Year is 1962 and then make a scatter plot comparing ‘CO2 emissions (metric tons per capita)’ and gdpPercap for the filtered data.
gapminder_clean %>%
  filter(Year == 1962) %>%
  ggplot(aes(x = `CO2 emissions (metric tons per capita)`, y = gdpPercap)) +
  geom_point()+
  theme_classic()
## Warning: Removed 151 rows containing missing values (geom_point).

  1. On the filtered data, calculate the correlation of ‘CO2 emissions (metric tons per capita)’ and gdpPercap. What is the correlation and associated p value?
filtered_data <- gapminder_clean %>%
  filter(Year == 1962)
correlation <- cor.test(filtered_data$gdpPercap, filtered_data$`CO2 emissions (metric tons per capita)`)

cat("The Correlation is: ", correlation$estimate, "\n P-VALUE is: ", correlation$p.value)
## The Correlation is:  0.9260817 
##  P-VALUE is:  1.128679e-46
  1. On the unfiltered data, answer “In what year is the correlation between ‘CO2 emissions (metric tons per capita)’ and gdpPercap the strongest?” Filter the dataset to that year for the next step
gapminder_clean %>%
  group_by(Year) %>%
  summarise(correlation = cor.test(`CO2 emissions (metric tons per capita)`, gdpPercap)$estimate) %>%
  arrange(desc(correlation)) %>%
  filter(correlation == max(correlation))
## # A tibble: 1 x 2
##    Year correlation
##   <dbl>       <dbl>
## 1  1967       0.939
filter_data_1967 <- gapminder_clean %>%
  filter(Year == 1967)

The year in which the correlation between Carbon dioxide and gdpPercap was the highest was 1967.

  1. Using plotly, create an interactive scatter plot comparing ‘CO2 emissions (metric tons per capita)’ and gdpPercap, where the point size is determined by pop (population) and the color is determined by the continent. You can easily convert any ggplot plot to a plotly plot using the ggplotly() command.
g <- ggplot(filter_data_1967, aes(x = `CO2 emissions (metric tons per capita)`, y = gdpPercap, color = continent, size = pop)) +
  geom_point()+
  theme_classic()
ggplotly(g)

Part 2

  1. What is the relationship between continent and ‘Energy use (kg of oil equivalent per capita)’? (stats test needed)
gapminder_clean %>%
  na.omit() %>%
  ggplot(aes(x = continent, y = `Energy use (kg of oil equivalent per capita)`)) +
  geom_boxplot() +
  scale_y_log10()+
  theme_classic()

Select Continent and Energy use (kg of oil equivalent per capita) Columns

continent_data <- gapminder_clean %>%
  na.omit() %>%
  select(continent, `Energy use (kg of oil equivalent per capita)`)
continent_data %>%
  group_by(continent) %>%
  summarise(Mean_Energy_use = mean(`Energy use (kg of oil equivalent per capita)`, na.rm = TRUE)) %>%
  arrange(Mean_Energy_use)
## # A tibble: 5 x 2
##   continent Mean_Energy_use
##   <chr>               <dbl>
## 1 Africa               665.
## 2 Americas            1359.
## 3 Asia                1521.
## 4 Europe              3517.
## 5 Oceania             4506.

To Check the relationships between Continents in terms of Energy use, i will apply Analysis of Variance (ANOVA). this is because i am comparing means of more than two groups.

continent_data %>%
  aov(`Energy use (kg of oil equivalent per capita)` ~ continent, data = .) %>%
  summary()
##              Df    Sum Sq   Mean Sq F value Pr(>F)    
## continent     4 6.798e+08 169938717   58.32 <2e-16 ***
## Residuals   516 1.504e+09   2914122                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
continent_data %>%
  aov(`Energy use (kg of oil equivalent per capita)` ~ continent, data = .) %>%
  TukeyHSD()
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = `Energy use (kg of oil equivalent per capita)` ~ continent, data = .)
## 
## $continent
##                       diff       lwr       upr     p adj
## Americas-Africa   693.7245  106.4060 1281.0429 0.0113441
## Asia-Africa       855.6667  276.9503 1434.3831 0.0005699
## Europe-Africa    2852.5003 2272.6061 3432.3945 0.0000000
## Oceania-Africa   3841.4070 2528.8619 5153.9521 0.0000000
## Asia-Americas     161.9422 -432.9740  756.8584 0.9456942
## Europe-Americas  2158.7758 1562.7138 2754.8379 0.0000000
## Oceania-Americas 3147.6825 1827.9146 4467.4504 0.0000000
## Europe-Asia      1996.8336 1409.2456 2584.4216 0.0000000
## Oceania-Asia     2985.7403 1669.7779 4301.7026 0.0000000
## Oceania-Europe    988.9067 -327.5741 2305.3874 0.2409293

Overall, there is a difference between continents in terms of energy consumption. This is noted using the p-value, which less than 0.05. Going by Continents, energy consumption between Oceania and Europe is not different, so is between America and Asia. as noted in the results above.

  1. Is there a significant difference between Europe and Asia with respect to ‘Imports of goods and services (% of GDP)’ in the years after 1990? (stats test needed)
Europe_Asia <-gapminder_clean %>% 
  filter(Year > 1990, continent %in% c("Europe", "Asia"))
 
Europe_Asia%>%
  ggplot(aes(x=continent, y=`Imports of goods and services (% of GDP)`))+
  geom_boxplot()+
  theme_classic()
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).

Europe_Asia <- droplevels(Europe_Asia)
t.test(Europe_Asia$`Imports of goods and services (% of GDP)` ~ continent, data = Europe_Asia)
## 
##  Welch Two Sample t-test
## 
## data:  Europe_Asia$`Imports of goods and services (% of GDP)` by continent
## t = 1.3552, df = 137.53, p-value = 0.1776
## alternative hypothesis: true difference in means between group Asia and group Europe is not equal to 0
## 95 percent confidence interval:
##  -2.321099 12.433240
## sample estimates:
##   mean in group Asia mean in group Europe 
##             46.84531             41.78924

The differences in Imports of goods and Services is not significant between Asia and Europe. this can be observed from the p-value which is greater 0.05, i fail to reject the null hypothesis which suggests that there is no difference.

  1. What is the country (or countries) that has the highest ‘Population density (people per sq. km of land area)’ across all years? (i.e., which country has the highest average ranking in this category across each time point in the dataset?)
average_pop_country_years <- gapminder_clean %>%
  group_by(`Country Name`, Year)%>%
  summarise(Average_per_year = mean(`Population density (people per sq. km of land area)`))%>%
  arrange(desc(Average_per_year))
## `summarise()` has grouped output by 'Country Name'. You can override using the `.groups` argument.
g <- ggplot(average_pop_country_years, aes(x = Year, y = Average_per_year, color = `Country Name`))+
  geom_line()+
  ggtitle("Population Density across all years")+
  ylab("Population density (people per sq. km of land area)'")+
  xlab("Year")+
  theme_classic()+
  theme(legend.position = "none")
ggplotly(g)
  1. What country (or countries) has shown the greatest increase in ‘Life expectancy at birth, total (years)’ since 1962?
life_exp_over_years <- gapminder_clean %>%
  select(Year, `Country Name` , `Life expectancy at birth, total (years)`) %>%
  filter(Year %in% c(min(Year),max(Year))) %>%
  spread(Year, `Life expectancy at birth, total (years)`)%>%
  mutate(pop_dif = `2007` - `1962`)%>%
  arrange(desc(pop_dif))%>%
  top_n(10,pop_dif)
datatable(life_exp_over_years)
top_high_expe_countries <- life_exp_over_years$`Country Name`
ggplo <- gapminder_clean%>%
  filter(`Country Name` %in% top_high_expe_countries)%>%
  ggplot(aes(x= Year, y = `Life expectancy at birth, total (years)`, color = `Country Name`))+
  geom_line()+
  geom_point()
ggplotly(ggplo)

It can be observed from the plot and the table that the countries Maldives and Bhutan have had an increase in life Expectancy since 1962. With Maldives having increased by 37 years While Bhutan increased by 33 years.